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Book ; Online: Subverting Stateful Firewalls with Protocol States (Extended Version)

Klein, Amit

2021

Abstract: We analyzed the generation of protocol header fields in the implementations of multiple TCP/IP network stacks and found new ways to leak information about global protocol states. We then demonstrated new covert channels by remotely observing and ... ...

Abstract	We analyzed the generation of protocol header fields in the implementations of multiple TCP/IP network stacks and found new ways to leak information about global protocol states. We then demonstrated new covert channels by remotely observing and modifying the system's global state via these protocol fields. Unlike earlier works, our research focuses on hosts that reside in firewalled networks (including source address validation -- SAV), which is a very common scenario nowadays. Our attacks are designed to be non-disruptive -- in the exfiltration scenario, this makes the attacks stealthier and thus extends their longevity, and in case of host alias resolution and similar techniques -- this ensures the techniques are ethical. We focused on ICMP, which is commonly served by firewalls, and on UDP, which is forecasted to take a more prominent share of the Internet traffic with the advent of HTTP/3 and QUIC, though we report results for TCP as well. The information leakage scenarios we discovered enable the construction of practical covert channels which directly pierce firewalls, or indirectly establish communication via hosts in firewalled networks that also employ SAV. We describe and test three novel attacks in this context: exfiltration via the firewall itself, exfiltration via a DMZ host, and exfiltration via co-resident containers. These are three generic, new use cases for covert channels that work around firewalling and enable devices that are not allowed direct communication with the Internet, to still exfiltrate data out of the network. In other words, we exfiltrate data from isolated networks to the Internet. We also explain how to mount known attacks such as host alias resolution, de-NATting and container co-residence detection, using the new information leakage techniques. Comment: A shorter version of this paper is to be presented in NDSS 2022. UPDATE 2021-12-25: Added CVE numbers for Linux and NetBSD. UPDATE 2022-04-13: Fixed some typos, missing words, clarified the meaning of connection-less, named ...
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2021-12-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Cross Layer Attacks and How to Use Them (for DNS Cache Poisoning, Device Tracking and More)

Klein, Amit

2020

Abstract: We analyze the prandom pseudo random number generator (PRNG) in use in the Linux kernel (which is the kernel of the Linux operating system, as well as of Android) and demonstrate that this PRNG is weak. The prandom PRNG is in use by many "consumers" in ... ...

Abstract	We analyze the prandom pseudo random number generator (PRNG) in use in the Linux kernel (which is the kernel of the Linux operating system, as well as of Android) and demonstrate that this PRNG is weak. The prandom PRNG is in use by many "consumers" in the Linux kernel. We focused on three consumers at the network level -- the UDP source port generation algorithm, the IPv6 flow label generation algorithm and the IPv4 ID generation algorithm. The flawed prandom PRNG is shared by all these consumers, which enables us to mount "cross layer attacks" against the Linux kernel. In these attacks, we infer the internal state of the prandom PRNG from one OSI layer, and use it to either predict the values of the PRNG employed by the other OSI layer, or to correlate it to an internal state of the PRNG inferred from the other protocol. Using this approach we can mount a very efficient DNS cache poisoning attack against Linux. We collect TCP/IPv6 flow label values, or UDP source ports, or TCP/IPv4 IP ID values, reconstruct the internal PRNG state, then predict an outbound DNS query UDP source port, which speeds up the attack by a factor of x3000 to x6000. This attack works remotely, but can also be mounted locally, across Linux users and across containers, and (depending on the stub resolver) can poison the cache with an arbitrary DNS record. Additionally, we can identify and track Linux and Android devices -- we collect TCP/IPv6 flow label values and/or UDP source port values and/or TCP/IPv4 ID fields, reconstruct the PRNG internal state and correlate this new state to previously extracted PRNG states to identify the same device. Comment: To be published in 2021 IEEE Symposium on Security and Privacy (SP)
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2020-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Transcriptome Analysis Identifies Oncogenic Tissue Remodeling during Progression from Common Nevi to Early Melanoma.

Zia, Amin / Litvin, Yoav / Voskoboynik, Ronnie / Klein, Amit / Shachaf, Catherine

The American journal of pathology

2023 Volume 193, Issue 7, Page(s) 995–1004

Abstract: Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histologic alterations ... ...

Abstract	Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histologic alterations within nevi and surrounding tissue. Herein, a comprehensive analysis of publicly available gene expression data sets of melanoma, common or congenital nevi (CN), and dysplastic nevi (DN), assessed molecular and genetic pathways leading to early melanoma. The results demonstrate several pathways reflective of ongoing local structural tissue remodeling activity likely involved during the transition from benign to early-stage melanoma. These processes include the gene expression of cancer-associated fibroblasts, collagens, extracellular matrix, and integrins, which assist early melanoma development and the immune surveillance that plays a substantial role at this early stage. Furthermore, genes up-regulated in DN were also overexpressed in melanoma tissue, supporting the notion that DN may serve as a transitional phase toward oncogenesis. CN collected from healthy individuals exhibited different gene signatures compared with histologically benign nevi tissue located adjacent to melanoma (adjacent nevi). Finally, the expression profile of microdissected adjacent nevi tissue was more similar to melanoma compared with CN, revealing the melanoma influence on this annexed tissue.
MeSH term(s)	Humans ; Melanoma/genetics ; Melanoma/pathology ; Nevus/genetics ; Nevus/pathology ; Skin Neoplasms/pathology ; Dysplastic Nevus Syndrome/genetics ; Dysplastic Nevus Syndrome/metabolism ; Dysplastic Nevus Syndrome/pathology ; Gene Expression Profiling
Language	English
Publishing date	2023-05-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2943-9
ISSN	1525-2191 ; 0002-9440
ISSN (online)	1525-2191
ISSN	0002-9440
DOI	10.1016/j.ajpath.2023.03.016
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Book ; Online: Device Tracking via Linux's New TCP Source Port Selection Algorithm (Extended Version)

Kol, Moshe / Klein, Amit / Gilad, Yossi

2022

Abstract: We describe a tracking technique for Linux devices, exploiting a new TCP source port generation mechanism recently introduced to the Linux kernel. This mechanism is based on an algorithm, standardized in RFC 6056, for boosting security by better ... ...

Abstract	We describe a tracking technique for Linux devices, exploiting a new TCP source port generation mechanism recently introduced to the Linux kernel. This mechanism is based on an algorithm, standardized in RFC 6056, for boosting security by better randomizing port selection. Our technique detects collisions in a hash function used in the said algorithm, based on sampling TCP source ports generated in an attacker-prescribed manner. These hash collisions depend solely on a per-device key, and thus the set of collisions forms a device ID that allows tracking devices across browsers, browser privacy modes, containers, and IPv4/IPv6 networks (including some VPNs). It can distinguish among devices with identical hardware and software, and lasts until the device restarts. We implemented this technique and then tested it using tracking servers in two different locations and with Linux devices on various networks. We also tested it on an Android device that we patched to introduce the new port selection algorithm. The tracking technique works in real-life conditions, and we report detailed findings about it, including its dwell time, scalability, and success rate in different network types. We worked with the Linux kernel team to mitigate the exploit, resulting in a security patch introduced in May 2022 to the Linux kernel, and we provide recommendations for better securing the port selection algorithm in the paper. Comment: This is an extended version of a paper with the same name that will be presented in the 32nd Usenix Security Symposium (USENIX 2023). UPDATE (2022-10-08): We revised some bibliography entries and clarified some aspects of the mathematical analysis. UPDATE (2022-12-22): Added Usenix 2023 artifact badges and fixed some typos
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2022-09-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study.

Tonn, Peter / Seule, Lea / Degani, Yoav / Herzinger, Shani / Klein, Amit / Schulze, Nina

JMIR formative research

2022 Volume 6, Issue 8, Page(s) e37061

Abstract: Background: Mood disorders and depression are pervasive and significant problems worldwide. These represent severe health and emotional impairments for individuals and a considerable economic and social burden. Therefore, fast and reliable diagnosis and ...

Abstract	Background: Mood disorders and depression are pervasive and significant problems worldwide. These represent severe health and emotional impairments for individuals and a considerable economic and social burden. Therefore, fast and reliable diagnosis and appropriate treatment are of great importance. Verbal communication can clarify the speaker's mental state-regardless of the content, via speech melody, intonation, and so on. In both everyday life and clinical conditions, a listener with appropriate previous knowledge or a trained specialist can grasp helpful knowledge about the speaker's psychological state. Using automated speech analysis for the assessment and tracking of patients with mental health issues opens up the possibility of remote, automatic, and ongoing evaluation when used with patients' smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective: The primary aim of this study is to evaluate the measurements of the presence or absence of depressive mood in participants by comparing the analysis of noncontentual speech parameters with the results of the Patient Health Questionnaire-9. Methods: This proof-of-concept study included participants in different affective phases (with and without depression). The inclusion criteria included a neurological or psychiatric diagnosis made by a specialist and fluent use of the German language. The measuring instrument was the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters based on machine learning and the assessment of the findings using Patient Health Questionnaire-9. Results: A total of 292 psychiatric and voice assessments were performed with 163 participants (males: n=47, 28.8%) aged 15 to 82 years. Of the 163 participants, 87 (53.3%) were not depressed at the time of assessment, and 88 (53.9%) participants had clinically mild to moderate depressive phases. Of the 163 participants, 98 (32.5%) showed subsyndromal symptoms, and 19 (11.7%) participants were severely depressed. In the speech analysis, a clear differentiation between the individual depressive levels, as seen in the Patient Health Questionnaire-9, was also shown, especially the clear differentiation between nondepressed and depressed participants. The study showed a Pearson correlation of 0.41 between clinical assessment and noncontentual speech analysis (P<.001). Conclusions: The use of speech analysis shows a high level of accuracy, not only in terms of the general recognition of a clinically relevant depressive state in the participants. Instead, there is a high degree of agreement regarding the extent of depressive impairment with the assessment of experienced clinical practitioners. From our point of view, the application of the noncontentual analysis system in everyday clinical practice makes sense, especially with the idea of a quick and unproblematic assessment of the state of mind, which can even be carried out without personal contact. Trial registration: ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008.
Language	English
Publishing date	2022-08-30
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/37061
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Book ; Online: From IP ID to Device ID and KASLR Bypass (Extended Version)

Klein, Amit / Pinkas, Benny

2019

Abstract: IP headers include a 16-bit ID field. Our work examines the generation of this field in Windows (versions 8 and higher), Linux and Android, and shows that the IP ID field enables remote servers to assign a unique ID to each device and thus be able to ... ...

Abstract	IP headers include a 16-bit ID field. Our work examines the generation of this field in Windows (versions 8 and higher), Linux and Android, and shows that the IP ID field enables remote servers to assign a unique ID to each device and thus be able to identify subsequent transmissions sent from that device. This identification works across all browsers and over network changes. In modern Linux and Android versions, this field leaks a kernel address, thus we also break KASLR. Our work includes reverse-engineering of the Windows IP ID generation code, and a cryptanalysis of this code and of the Linux kernel IP ID generation code. It provides practical techniques to partially extract the key used by each of these algorithms, overcoming different implementation issues, and observing that this key can identify individual devices. We deployed a demo (for Windows) showing that key extraction and machine fingerprinting works in the wild, and tested it from networks around the world. Comment: This is an extended paper. The original paper will appear in Usenix Security 2019
Keywords	Computer Science - Cryptography and Security ; D.4.6 ; E.3
Subject code	303
Publishing date	2019-06-25
Publishing country	us
Document type	Book ; Online
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Article ; Online: Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

Wasserzug, Yael / Degani, Yoav / Bar-Shaked, Mili / Binyamin, Milana / Klein, Amit / Hershko, Shani / Levkovitch, Yechiel

Journal of affective disorders

2022 Volume 325, Page(s) 627–632

Abstract: Background: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal ... ...

Abstract	Background: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. Methods: Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. Results: A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). Limitations: The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. Conclusions: The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.
MeSH term(s)	Humans ; Depressive Disorder, Major/diagnosis ; Depressive Disorder, Major/epidemiology ; Pandemics ; COVID-19 ; Speech ; Machine Learning
Language	English
Publishing date	2022-12-28
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2022.12.117
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Article: Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study.

Tonn, Peter / Degani, Yoav / Hershko, Shani / Klein, Amit / Seule, Lea / Schulze, Nina

JMIR research protocols

2020 Volume 9, Issue 5, Page(s) e13852

Abstract: Background: The prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient's mental ... ...

Abstract	Background: The prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient's mental health deteriorate. To ensure effective treatment, the level of experience of the physician or therapist is of importance, both in the initial diagnosis and in the treatment of mental illnesses. Nevertheless, experienced physicians and psychotherapists are not available in enough numbers everywhere, especially in rural areas or in less developed countries. Human speech can reveal a speaker's mental state by altering its noncontent aspects (speech melody, intonations, speech rate, etc). This is noticeable in both the clinic and everyday life by having prior knowledge of the normal speech patterns of the affected person, and with enough time spent listening to the patient. However, this time and experience are often unavailable, leaving unused opportunities to capture linguistic, noncontent information. To improve the care of patients with mental disorders, we have developed a concept for assessing their most important mental parameters through a noncontent analysis of their active speech. Using speech analysis for the assessment and tracking of mental health patients opens up the possibility of remote, automatic, and ongoing evaluation when used with patients' smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective: The primary objective of this study is to evaluate measurements of participants' mental state by comparing the analysis of noncontent speech parameters to the results of several psychological questionnaires (Symptom Checklist-90 [SCL-90], the Patient Health Questionnaire [PHQ], and the Big 5 Test). Methods: In this paper, we described a case-controlled study (with a case group and one control group). The participants will be recruited in an outpatient neuropsychiatric treatment center. Inclusion criteria are a neurological or psychiatric diagnosis made by a specialist, no terminal or life-threatening illnesses, and fluent use of the German language. Exclusion criteria include psychosis, dementia, speech or language disorders in neurological diseases, addiction history, a suicide attempt recently or in the last 12 months, or insufficient language skills. The measuring instrument will be the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters, and the assessment of findings using psychometric instruments and questionnaires (SCL-90, PHQ, Big 5 Test). Results: The study is ongoing as of September 2019, but we have enrolled 254 participants. There have been 161 measurements completed at timepoint 1, and a total of 62 participants have completed every psychological and speech analysis measurement. Conclusions: It appears that the tone and modulation of speech are as important, if not more so, than the content, and should not be underestimated. This is particularly evident in the interpretation of the psychological findings thus far acquired. Therefore, the application of a software analysis tool could increase the accuracy of finding assessments and improve patient care. Trial registration: ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008. International registered report identifier (irrid): PRR1-10.2196/13852.
Language	English
Publishing date	2020-05-14
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719222-2
ISSN	1929-0748
ISSN	1929-0748
DOI	10.2196/13852
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Article ; Online: Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs.

Hernandez Hernandez, Diana / Ding, Lin / Murao, Ayako / Dahlin, Lukas R / Li, Gabriella / Arnolds, Kathleen L / Amezola, Melissa / Klein, Amit / Mitra, Aishwarya / Mecacci, Sonia / Linger, Jeffrey G / Guarnieri, Michael T / Suzuki, Yo

ACS synthetic biology

2023 Volume 12, Issue 9, Page(s) 2778–2782

Abstract: Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The ... ...

Abstract	Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.
MeSH term(s)	High-Throughput Nucleotide Sequencing ; DNA/genetics
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2023-08-15
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ISSN	2161-5063
ISSN (online)	2161-5063
DOI	10.1021/acssynbio.3c00183
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Article ; Online: Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.

Klein, Amit / Puldon, Karena / Dilchert, Stephan / Hartogensis, Wendy / Chowdhary, Anoushka / Anglo, Claudine / Pandya, Leena S / Hecht, Frederick M / Mason, Ashley E / Smarr, Benjamin L

Frontiers in big data

2022 Volume 5, Page(s) 1043704

Abstract: Background: Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also ... ...

Abstract	Background: Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Objective: Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. Methods: We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Results: Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Conclusion: Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.
Language	English
Publishing date	2022-11-10
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2022.1043704
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